Computer analysis of gallbladder ultrasonic images towards recognition of pathological lesions

2011 ◽  
Vol 19 (2) ◽  
Author(s):  
M. Ogiela ◽  
S. Bodzioch
Keyword(s):  
Image Processing ◽  
Line Profile ◽  
Computer Analysis ◽  
Heuristic Method ◽  
Automatic Detection ◽  
Disease Symptoms ◽  
New Approach ◽  
Pathological Lesions ◽  
Major Stage ◽  
Ultrasonic Images

AbstractThis paper presents a new approach to gallbladder ultrasonic image processing and analysis towards automatic detection and interpretation of disease symptoms on processed US images. First, in this paper, there is presented a new heuristic method of filtering gallbladder contours from images. A major stage in this filtration is to segment and section off areas occupied by the said organ. This paper provides for an inventive algorithm for the holistic extraction of gallbladder image contours, based on rank filtration, as well as on the analysis of line profile sections on tested organs. The second part concerns detecting the most important lesion symptoms of the gallbladder. Automating a process of diagnosis always comes down to developing algorithms used to analyze the object of such diagnosis and verify the occurrence of symptoms related to given affection. The methodology of computer analysis of US gallbladder images presented here is clearly utilitarian in nature and after standardising can be used as a technique for supporting the diagnostics of selected gallbladder disorders using the images of this organ.

scholarly journals A New Approach to Reducing the Distortion of the Digital Image Natural Model in the DCT Domain When Embedding Information According to the QIM Method

2019 ◽  
Author(s):  
Олег Евсютин ◽  
Oleg Evsutin ◽  
Анна Мельман ◽  
Anna Melman ◽  
Роман Мещеряков ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Digital Content ◽  
The Internet ◽  
Embedding Capacity ◽  
New Approach ◽  
Additional Information ◽  
Dct Domain ◽  
Dct Coefficients ◽  
Information Confidentiality

One of the areas of digital image processing is the steganographic embedding of additional information into them. Digital steganography methods are used to ensure the information confidentiality, as well as to track the distribution of digital content on the Internet. Main indicators of the steganographic embedding effectiveness are invisibility to the human eye, characterized by the PSNR metric, and embedding capacity. However, even with full visual stealth of embedding, its presence may produce a distortion of the digital image natural model in the frequency domain. The article presents a new approach to reducing the distortion of the digital image natural model in the field of discrete cosine transform (DCT) when embedding information using the classical QIM method. The results of the experiments show that the proposed approach allows reducing the distortion of the histograms of the distribution of DCT coefficients, and thereby eliminating the unmasking signs of embedding.

SEGMENTATION OF ULTRASONIC IMAGES WITH NEURAL NETWORKS

1991 ◽  
Vol 05 (04) ◽  
pp. 619-628 ◽  
Author(s):  
RONALD H. SILVERMAN
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Back Propagation ◽  
Three Dimensional ◽  
Competitive Learning ◽  
Data Sets ◽  
Image Structure ◽  
Ultrasonic Images ◽  
Traditional Approaches

Neural networks differ from traditional approaches to image processing in terms of their ability to adapt to regularities in image structure and to self-organize so as to implement directed transformations. Biomedical ultrasonic images are often degraded in quality by noise and other factors, making enhancement techniques particularly important. This paper describes use of back propagation and competitive learning for enhancement and segmentation of ultrasonic images of the eye. Of particular interest is the extension or these technique to segmentation of three-dimensional data sets, where simple thresholding and gradient operations are not entirely successful.

scholarly journals An Automatic Method to Detect and Measure Leaf Disease Symptoms Using Digital Image Processing

Plant Disease ◽  
2014 ◽  
Vol 98 (12) ◽  
pp. 1709-1716 ◽  
Author(s):  
Jayme Garcia Arnal Barbedo
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Human Error ◽  
Automatic Method ◽  
False Negatives ◽  
Image Capture ◽  
Extrinsic Factors ◽  
Disease Symptoms ◽  
Leaf Veins ◽  
Leaf Symptoms

A method is presented to detect and quantify leaf symptoms using conventional color digital images. The method was designed to be completely automatic, eliminating the possibility of human error and reducing time taken to measure disease severity. The program is capable of dealing with images containing multiple leaves, further reducing the time taken. Accurate results are possible when the symptoms and leaf veins have similar color and shade characteristics. The algorithm is subject to one constraint: the background must be as close to white or black as possible. Tests showed that the method provided accurate estimates over a wide variety of conditions, being robust to variation in size, shape, and color of leaves; symptoms; and leaf veins. Low rates of false positives and false negatives occurred due to extrinsic factors such as issues with image capture and the use of extreme file compression ratios.

Image Processing Strategies for Automatic Detection of Common Gastroenterological Diseases

2018 ◽  
Author(s):  
Rafael Neujahr Copstein ◽  
Vicenzo Abichequer ◽  
Matheus Cruz Andrade ◽  
Lucas Almeida Machado ◽  
Evandro Rodrigues ◽  
...  
Keyword(s):  
Image Processing ◽  
Automatic Detection ◽  
Processing Strategies

An Approach for Automatic Detection and Grading of Macular Edema

2017 ◽  
pp. 1677-1702
Author(s):  
Jyoti Prakash Medhi
Keyword(s):  
Image Processing ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Macular Edema ◽  
Automatic Detection ◽  
Automatic Analysis ◽  
Fundus Images

Prolonged Diabetes causes massive destruction to the retina, known as Diabetic Retinopathy (DR) leading to blindness. The blindness due to DR may consequence from several factors such as Blood vessel (BV) leakage, new BV formation on retina. The effects become more threatening when abnormalities involves the macular region. Here automatic analysis of fundus images becomes important. This system checks for any abnormality and help ophthalmologists in decision making and to analyze more number of cases. The main objective of this chapter is to explore image processing tools for automatic detection and grading macular edema in fundus images.

New Trends of Image Analysis in the Medical Field

1988 ◽  
Vol 27 (02) ◽  
pp. 53-57 ◽  
Author(s):  
J. Dengler ◽  
H. Bertsch ◽  
J. F. Desaga ◽  
M. Schmidt
Keyword(s):  
Image Processing ◽  
Cancer Research ◽  
Image Analysis ◽  
A Priori ◽  
Research Work ◽  
A Priori Knowledge ◽  
German Cancer Research ◽  
Medical Field ◽  
New Approach ◽  
Biological Field

SummaryImage analysis with the aid of the computer has rapidly developed over the last few years. There are many possibilities of making use of this development in the medical and biological field. This paper is meant to give a rather general overview of recent systematics regarding the existing methodology in image analysis. Furthermore, some parts of these systematics are illustrated in greater detail by recent research work in the German Cancer Research Center. In particular, two applications are reported where special emphasis is laid on mathematical morphology. This relatively new approach to image analysis finds growing interest in the image processing community and has its strength in bridging the gap between a priori knowledge and image analysis procedures.

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